Increased sinusoidal export of drug glucuronides is a compensative mechanism in liver cirrhosis of mice

Rationale: Liver cirrhosis is known to affect drug pharmacokinetics, but the functional assessment of the underlying pathophysiological alterations in drug metabolism is difficult. Methods: Cirrhosis in mice was induced by repeated treatment with carbon tetrachloride for 12 months. A cocktail of six drugs was administered, and parent compounds as well as phase I and II metabolites were quantified in blood, bile, and urine in a time-dependent manner. Pharmacokinetics were modeled in relation to the altered expression of metabolizing enzymes. In discrepancy with computational predictions, a strong increase of glucuronides in blood was observed in cirrhotic mice compared to vehicle controls. Results: The deviation between experimental findings and computational simulations observed by analyzing different hypotheses could be explained by increased sinusoidal export and corresponded to increased expression of export carriers (Abcc3 and Abcc4). Formation of phase I metabolites and clearance of the parent compounds were surprisingly robust in cirrhosis, although the phase I enzymes critical for the metabolism of the administered drugs in healthy mice, Cyp1a2 and Cyp2c29, were downregulated in cirrhotic livers. RNA-sequencing revealed the upregulation of numerous other phase I metabolizing enzymes which may compensate for the lost CYP isoenzymes. Comparison of genome-wide data of cirrhotic mouse and human liver tissue revealed similar features of expression changes, including increased sinusoidal export and reduced uptake carriers. Conclusion: Liver cirrhosis leads to increased blood concentrations of glucuronides because of increased export from hepatocytes into the sinusoidal blood. Although individual metabolic pathways are massively altered in cirrhosis, the overall clearance of the parent compounds was relatively robust due to compensatory mechanisms.

Tolerance of repeated toxic injuries of murine livers is associated with steatosis and inflammation

The human liver has a remarkable capacity to regenerate and thus compensate over decades for fibrosis caused by toxic chemicals, drugs, alcohol, or malnutrition. To date, no protective mechanisms have been identified that help the liver tolerate these repeated injuries. In this study, we revealed dysregulation of lipid metabolism and mild inflammation as protective mechanisms by studying longitudinal multi-omic measurements of liver fibrosis induced by repeated CCl₄ injections in mice (n = 45). Based on comprehensive proteomics, transcriptomics, blood- and tissue-level profiling, we uncovered three phases of early disease development-initiation, progression, and tolerance. Using novel multi-omic network analysis, we identified multi-level mechanisms that are significantly dysregulated in the injury-tolerant response. Public data analysis shows that these profiles are altered in human liver diseases, including fibrosis and early cirrhosis stages. Our findings mark the beginning of the tolerance phase as the critical switching point in liver response to repetitive toxic doses. After fostering extracellular matrix accumulation as an acute response, we observe a deposition of tiny lipid droplets in hepatocytes only in the Tolerant phase. Our comprehensive study shows that lipid metabolism and mild inflammation may serve as biomarkers and are putative functional requirements to resist further disease progression.

Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and therapeutic options for advanced HCC are limited. Here, we observe that intestinal dysbiosis affects antitumor immune surveillance and drives liver disease progression towards cancer. Dysbiotic microbiota, as seen in Nlrp6^−/− mice, induces a Toll-like receptor 4 dependent expansion of hepatic monocytic myeloid-derived suppressor cells (mMDSC) and suppression of T-cell abundance. This phenotype is transmissible via fecal microbiota transfer and reversible upon antibiotic treatment, pointing to the high plasticity of the tumor microenvironment. While loss of Akkermansia muciniphila correlates with mMDSC abundance, its reintroduction restores intestinal barrier function and strongly reduces liver inflammation and fibrosis. Cirrhosis patients display increased bacterial abundance in hepatic tissue, which induces pronounced transcriptional changes, including activation of fibro-inflammatory pathways as well as circuits mediating cancer immunosuppression. This study demonstrates that gut microbiota closely shapes the hepatic inflammatory microenvironment opening approaches for cancer prevention and therapy.

Steatohepatitis is a chronic hepatic inflammation associated with increased risk of hepatocellular carcinoma progression. Here the authors show that intestinal dysbiosis in mice lacking the inflammasome sensor molecule NLRP6 aggravates steatohepatitis and accelerates liver cancer progression, a process that can be delayed by antibiotic treatment.

decoupleR: ensemble of computational methods to infer biological activities from omics data

Summary

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators.

Availability and implementation

decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

OUP accepted manuscript

Motivation

Omics data are broadly used to get a snapshot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signalling networks.

Results

We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of transcriptomics, phosphoproteomics and metabolomics data, either from bulk or single-cell experiments. FUNKI also features different options to visualize the results and run post-analyses, and is mirrored as a scripted version in R.

Availability and implementation

FUNKI is a free and open-source application built on R and Shiny, available at https://github.com/saezlab/ShinyFUNKI and https://saezlab.shinyapps.io/funki/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Spatio-Temporal Multiscale Analysis of Western Diet-Fed Mice Reveals a Translationally Relevant Sequence of Events during NAFLD Progression

Mouse models of non-alcoholic fatty liver disease (NAFLD) are required to define therapeutic targets, but detailed time-resolved studies to establish a sequence of events are lacking. Here, we fed male C57Bl/6N mice a Western or standard diet over 48 weeks. Multiscale time-resolved characterization was performed using RNA-seq, histopathology, immunohistochemistry, intravital imaging, and blood chemistry; the results were compared to human disease. Acetaminophen toxicity and ammonia metabolism were additionally analyzed as functional readouts. We identified a sequence of eight key events: formation of lipid droplets; inflammatory foci; lipogranulomas; zonal reorganization; cell death and replacement proliferation; ductular reaction; fibrogenesis; and hepatocellular cancer. Functional changes included resistance to acetaminophen and altered nitrogen metabolism. The transcriptomic landscape was characterized by two large clusters of monotonously increasing or decreasing genes, and a smaller number of 'rest-and-jump genes' that initially remained unaltered but became differentially expressed only at week 12 or later. Approximately 30% of the genes altered in human NAFLD are also altered in the present mouse model and an increasing overlap with genes altered in human HCC occurred at weeks 30-48. In conclusion, the observed sequence of events recapitulates many features of human disease and offers a basis for the identification of therapeutic targets.

Transcriptomic Cross-Species Analysis of Chronic Liver Disease Reveals Consistent Regulation Between Humans and Mice

Mouse models are frequently used to study chronic liver diseases (CLDs). To assess their translational relevance, we quantified the similarity of commonly used mouse models to human CLDs based on transcriptome data. Gene‐expression data from 372 patients were compared with data from acute and chronic mouse models consisting of 227 mice, and additionally to nine published gene sets of chronic mouse models. Genes consistently altered in humans and mice were mapped to liver cell types based on single‐cell RNA‐sequencing data and validated by immunostaining. Considering the top differentially expressed genes, the similarity between humans and mice varied among the mouse models and depended on the period of damage induction. The highest recall (0.4) and precision (0.33) were observed for the model with 12‐months damage induction by CCl₄ and by a Western diet, respectively. Genes consistently up‐regulated between the chronic CCl₄ model and human CLDs were enriched in inflammatory and developmental processes, and mostly mapped to cholangiocytes, macrophages, and endothelial and mesenchymal cells. Down‐regulated genes were enriched in metabolic processes and mapped to hepatocytes. Immunostaining confirmed the regulation of selected genes and their cell type specificity. Genes that were up‐regulated in both acute and chronic models showed higher recall and precision with respect to human CLDs than exclusively acute or chronic genes. Conclusion: Similarly regulated genes in human and mouse CLDs were identified. Despite major interspecies differences, mouse models detected 40% of the genes significantly altered in human CLD. The translational relevance of individual genes can be assessed at https://saezlab.shinyapps.io/liverdiseaseatlas/.

Consensus Transcriptional Landscape of Human End-Stage Heart Failure

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.

Hepatocyte-specific NRF2 activation controls fibrogenesis and carcinogenesis in steatohepatitis

BACKGROUND & AIMS

In chronic liver diseases, inflammation induces oxidative stress and thus may contribute to the progression of liver injury, fibrosis, and carcinogenesis. The KEAP1/NRF2 axis is a major regulator of cellular redox balance. In the present study, we investigated whether the KEAP1/NRF2 system is involved in liver disease progression in humans and mice.

METHODS

The clinical relevance of oxidative stress was investigated by liver RNA sequencing in a well-characterized cohort of patients with non-alcoholic fatty liver disease (n = 63) and correlated with histological and clinical parameters. For functional analysis, hepatocyte-specific Nemo knockout (NEMO^Δhepa) mice were crossed with hepatocyte-specific Keap1 knockout (KEAP1^Δhepa) mice.

RESULTS

Immunohistochemical analysis of human liver sections showed increased oxidative stress and high NRF2 expression in patients with chronic liver disease. RNA sequencing of liver samples in a human pediatric NAFLD cohort revealed a significant increase of NRF2 activation correlating with the grade of inflammation, but not with the grade of steatosis, which could be confirmed in a second adult NASH cohort. In mice, microarray analysis revealed that Keap1 deletion induces NRF2 target genes involved in glutathione metabolism and xenobiotic stress (e.g., Nqo1). Furthermore, deficiency of one of the most important antioxidants, glutathione (GSH), in NEMO^Δhepa livers was rescued after deleting Keap1. As a consequence, NEMO^Δhepa/KEAP1^Δhepa livers showed reduced apoptosis compared to NEMO^Δhepa livers as well as a dramatic downregulation of genes involved in cell cycle regulation and DNA replication. Consequently, NEMO^Δhepa/KEAP1^Δhepa compared to NEMO^Δhepa livers displayed decreased fibrogenesis, lower tumor incidence, reduced tumor number, and decreased tumor size.

CONCLUSIONS

NRF2 activation in patients with non-alcoholic steatohepatitis correlates with the grade of inflammation, but not steatosis. Functional analysis in mice demonstrated that NRF2 activation in chronic liver disease is protective by ameliorating fibrogenesis, initiation and progression of hepatocellular carcinogenesis.

LAY SUMMARY

The KEAP1 (Kelch-like ECH-associated protein-1)/NRF2 (erythroid 2-related factor 2) axis has a major role in regulating cellular redox balance. Herein, we show that NRF2 activity correlates with the grade of inflammation in patients with non-alcoholic steatohepatitis. Functional studies in mice actually show that NRF2 activation, resulting from KEAP1 deletion, protects against fibrosis and cancer.

Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

BACKGROUND

Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.

RESULTS

To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.

CONCLUSIONS

Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.

Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction

Transcriptional perturbation signatures are valuable data sources for functional genomics. Linking perturbation signatures to screenings opens the possibility to model cellular phenotypes from expression data and to identify efficacious drugs. We linked perturbation transcriptomics data from the LINCS-L1000 project with cell viability information upon genetic (Achilles project) and chemical (CTRP screen) perturbations yielding more than 90 000 signature–viability pairs. An integrated analysis showed that the cell viability signature is a major factor underlying perturbation signatures. The signature is linked to transcription factors regulating cell death, proliferation and division time. We used the cell viability–signature relationship to predict viability from transcriptomics signatures, and identified and validated compounds that induce cell death in tumor cell lines. We showed that cellular toxicity can lead to unexpected similarity of signatures, confounding mechanism of action discovery. Consensus compound signatures predicted cell-specific drug sensitivity, even if the signature is not measured in the same cell line, and outperformed conventional drug-specific features. Our results can help in understanding mechanisms behind cell death and removing confounding factors of transcriptomic perturbation screens. To interactively browse our results and predict cell viability in new gene expression samples, we developed CEVIChE (CEll VIability Calculator from gene Expression; https://saezlab.shinyapps.io/ceviche/).

Influence of Liver Fibrosis on Lobular Zonation

Little is known about how liver fibrosis influences lobular zonation. To address this question, we used three mouse models of liver fibrosis, repeated CCl4 administration for 2, 6 and 12 months to induce pericentral damage, as well as bile duct ligation (21 days) and mdr2-/- mice to study periportal fibrosis. Analyses were performed by RNA-sequencing, immunostaining of zonated proteins and image analysis. RNA-sequencing demonstrated a significant enrichment of pericentral genes among genes downregulated by CCl4; vice versa, periportal genes were enriched among the upregulated genes. Immunostaining showed an almost complete loss of pericentral proteins, such as cytochrome P450 enzymes and glutamine synthetase, while periportal proteins, such as arginase 1 and CPS1 became expressed also in pericentral hepatocytes. This pattern of fibrosis-associated 'periportalization' was consistently observed in all three mouse models and led to complete resistance to hepatotoxic doses of acetaminophen (200 mg/kg). Characterization of the expression response identified the inflammatory pathways TGFβ, NFκB, TNFα, and transcription factors NFKb1, Stat1, Hif1a, Trp53, and Atf1 among those activated, while estrogen-associated pathways, Hnf4a and Hnf1a, were decreased. In conclusion, liver fibrosis leads to strong alterations of lobular zonation, where the pericentral region adopts periportal features. Beside adverse consequences, periportalization supports adaptation to repeated doses of hepatotoxic compounds.

A Functional Landscape of CKD Entities From Public Transcriptomic Data

Introduction

To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community.

Methods

We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure.

Results

We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration-approved drug nilotinib.

Conclusion

These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data.

Benchmark and integration of resources for the estimation of human transcription factor activities

The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF-target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.

Clinical ehrlichiosis in a cat

Clinical ehrlichiosis was diagnosed in a cat from Colorado on the basis of cytologic, serologic, and clinical findings. Clusters of gram-negative organisms that were morphologically similar to morulae of Ehrlichia spp were found only in mononuclear cells. The cat had clinical signs that were referable to infection by a rickettsial organism, and antibodies against E canis (titer, 1:80) and E risticii (titer, 1:40) were detected in serum. Exclusion of other obvious causes inducing similar clinical signs, and positive clinical response to doxycycline, an antibiotic with known antirickettsial actions, aided in diagnosis. The cat was clinically normal within days following initiation of treatment, and was clinically normal, as well as seronegative for antibodies against E canis and E risticii, 1,365 days after discharge.

Effect of dipyridamole on ischemia-induced changes in cardiac performance and metabolism

The effect of the coronary vasocilator agents dipyridamole and nifedapine on ischemia-induced changes in cardiac performance and coronary flow was studied in extra-corporeal-circulation supported, open-chest dogs during cardio-pulmonary bypass. Coronary flow and isovolumic cardiac performance were measured prior to clamping the aorta to induce global ischemic for 60 min and again after 30 min of reperfusion. Both agents given prior to ischemia significantly enhanced the coronary flow measured 30 min after reperfusion. Almost complete complete return of pre-ischemic left ventricular dp/dt and diastolic compliance was seen if dipyridamole was administered prior to the onset of ischemia. Dipridamole administered at the end of the ischemic period and prior to reperfusion also prevented the ischemia-induced fall in dp/dt but not the decrease in compliance. Myocardial high energy phosphate compounds decreased markedly during global ischemia and dipyridamole administration had no effect on the rate or extent of their depletion. Nifedapine administration led to even greater post-ischemic coronary vasodilation; however, the ischemia-induced decline in myocardial performance was not prevented.